Design and Optimization of Task-driven Dynamic Scalable Network Architecture in Spatial Information Networks

HE Lijun; JIA Ziye; LI Shiyin; WANG Yanting; WANG Li; LIU Lei

doi:10.11999/JEIT240505

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 >

HE Lijun, JIA Ziye, LI Shiyin, WANG Yanting, WANG Li, LIU Lei. Design and Optimization of Task-driven Dynamic Scalable Network Architecture in Spatial Information Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240505

Citation:

HE Lijun, JIA Ziye, LI Shiyin, WANG Yanting, WANG Li, LIU Lei. Design and Optimization of Task-driven Dynamic Scalable Network Architecture in Spatial Information Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240505

Citation:

HE Lijun, JIA Ziye, LI Shiyin, WANG Yanting, WANG Li, LIU Lei. Design and Optimization of Task-driven Dynamic Scalable Network Architecture in Spatial Information Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240505

PDF( 6353 KB)

Design and Optimization of Task-driven Dynamic Scalable Network Architecture in Spatial Information Networks

doi: 10.11999/JEIT240505

HE Lijun¹,
JIA Ziye²,
LI Shiyin^{1
,
,},
WANG Yanting³,
WANG Li³,
LIU Lei⁴

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
2.
School of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210024, China
3.
School of Software, Northwestern Polytechnical University, Xi’an 710072, China
4.
School of Electronic and Communication Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

Funds: The National Natural Science Foundation of China (62201463, 61901388), The Natural Science Foundation of Jiangsu Province of China (BK20220883)

Received Date: 2024-06-19
Rev Recd Date: 2024-11-17

Available Online: 2024-11-29

Abstract

Abstract

At the present stage, the satellite subsystems in Space Information Networks (SINs) have their own systems and are separated from each other, which makes the network appear closed and fragmented, forming a severe resource barrier and resulting in weak collaborative application ability of space resources and low network expansion ability. The traditional architecture design adopts the “completely subversive” idea of the current space networks, which greatly increases the difficulty of actual deployment. Therefore, based on the current status of satellite networks, the idea of “upgrading step by step” is adopted to promote the evolution of the existing network architecture, and a dynamic and scalable architecture model is proposed in SINs from the perspective of mission drive, so as to realize the efficient and dynamic sharing of space resources among subsystems and promote the dynamic and efficient aggregation of space resources according to the changes in mission requirements. Firstly, a phased network architecture model is proposed, aiming at compatibility and upgrading of the existing network architecture. Then, the design of the core component coordinator is introduced, including network structure and working protocol, superframe structure, and efficient network resource allocation strategy, to realize the efficient transmission of spatial data. The simulation results show that the proposed network architecture realizes the efficient sharing of network resources and greatly improves the utilization rate of network resources.
- Spatial information networks,
- Network architecture,
- Resource allocation,
- Data transmission

FullText(HTML)

References(26)

References

[1]	HU Fei, YANG Chaowei, SCHNASE J L, et al. Climatespark: An in-memory distributed computing framework for big climate data analytics[J]. Computers & Geosciences, 2018, 115: 154–166. doi: 10.1016/j.cageo.2018.03.011.
[2]	LI Deren.On the space and sky information real-time intelligent service system with deep integration of military and civilian[J].Civil-Military Integration on Cyberspace, 2018(12):12-15. LI Deren.On the space and sky information real-time intelligent service system with deep integration of military and civilian[J].Civil-Military Integration on Cyberspace, 2018(12):12-15.
[3]	张威, 张更新, 苟亮. 空间信息网络中的星座设计方法研究[J]. 中兴通讯技术, 2016, 22(4): 19–23,45. doi: 10.3969/j.issn.1009-6868.2016.04.004. ZHANG Wei, ZHANG Gengxin, and GOU Liang. Satellite constellation design in space information network[J]. ZTE Technology Journal, 2016, 22(4): 19–23,45. doi: 10.3969/j.issn.1009-6868.2016.04.004.
[4]	ZHANG Wei, ZHANG Gengxin, XIE Zhidong, et al. A hierarchical autonomous system based space information network architecture and topology control[J]. Journal of Communications and Information Networks, 2016, 1(3): 77–89. doi: 10.11959/j.issn.2096-1081.2016.017.
[5]	SUN Jiayu, QI Weijing, SONG Qingyang, et al. A new architecture for space information networks based on an MEO constellation optical backbone network[C]. Asia Communications and Photonics Conference 2017, Guangzhou, China, 2017: Su3C. 3. doi: 10.1364/ACPC.2017.Su3C.3.
[6]	MA Ting, QIAN Bo, QIN Xiaohan, et al. Satellite-terrestrial integrated 6G: An ultra-dense LEO networking management architecture[J]. IEEE Wireless Communications, 2024, 31(1): 62–69. doi: 10.1109/MWC.011.2200198.
[7]	ZHENG Gao, WANG Ning, and TAFAZOLLI R R. SDN in space: A virtual data-plane addressing scheme for supporting LEO satellite and terrestrial networks integration[J]. IEEE/ACM Transactions on Networking, 2024, 32(2): 1781–1796. doi: 10.1109/TNET.2023.3330672.
[8]	YANG Huiting, LIU Wei, WANG Xiangfeng, et al. Group sparse space information network with joint virtual network function deployment and maximum flow routing strategy[J]. IEEE Transactions on Wireless Communications, 2023, 22(8): 5291–5305. doi: 10.1109/TWC.2022.3233067.
[9]	XIA Qiufen, WANG Guijie, XU Zichuan, et al. Efficient algorithms for service chaining in NFV-enabled satellite edge networks[J]. IEEE Transactions on Mobile Computing, 2024, 23(5): 5677–5694. doi: 10.1109/TMC.2023.3312352.
[10]	BAO Jinzhen, ZHAO Baokang, YU Wangrong, et al. OpenSAN: A software-defined satellite network architecture[C]. Proceedings of the 2014 ACM Conference on SIGCOMM, Chicago, USA, 2014: 347–348. doi: 10.1145/2619239.2631454.
[11]	YANG Bowei, WU Yue, CHU Xiaoli, et al. Seamless handover in software-defined satellite networking[J]. IEEE Communications Letters, 2016, 20(9): 1768–1771. doi: 10.1109/LCOMM.2016.2585482.
[12]	FENG Jing, JIANG Lei, SHEN Ye, et al. A scheme for software defined ORS satellite networking[C]. 2014 IEEE Fourth International Conference on Big Data and Cloud Computing, Sydney, Australia, 2014: 716–721. doi: 10.1109/BDCloud.2014.19.
[13]	FENG Bohao, ZHOU Huachun, ZHANG Hongke, et al. HetNet: A flexible architecture for heterogeneous satellite-terrestrial networks[J]. IEEE Network, 2017, 31(6): 86–92. doi: 10.1109/MNET.2017.1600330.
[14]	SHENG Min, WANG Yu, LI Jiandong, et al. Toward a flexible and reconfigurable broadband satellite network: Resource management architecture and strategies[J]. IEEE Wireless Communications, 2017, 24(4): 127–133. doi: 10.1109/MWC.2017.1600173.
[15]	LI Taixin, ZHOU Huachun, LUO Hongbin, et al. SERvICE: A software defined framework for integrated space-terrestrial satellite communication[J]. IEEE Transactions on Mobile Computing, 2018, 17(3): 703–716. doi: 10.1109/TMC.2017.2732343.
[16]	ZHANG Ning, ZHANG Shan, YANG Peng, et al. Software defined space-air-ground integrated vehicular networks: Challenges and solutions[J]. IEEE Communications Magazine, 2017, 55(7): 101–109. doi: 10.1109/MCOM.2017.1601156.
[17]	SHI Yongpeng, CAO Yurui, LIU Jiajia, et al. A cross-domain SDN architecture for multi-layered space-terrestrial integrated networks[J]. IEEE Network, 2019, 33(1): 29–35. doi: 10.1109/MNET.2018.1800191.
[18]	CHEN Chen, LIAO Zhan, JU Ying, et al. Hierarchical domain-based multicontroller deployment strategy in SDN-enabled space-air-ground integrated network[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(6): 4864–4879. doi: 10.1109/TAES.2022.3199191.
[19]	ESMAT H H, LORENZO B, and SHI Weisong. Toward resilient network slicing for satellite-terrestrial edge computing IoT[J]. IEEE Internet of Things Journal, 2023, 10(16): 14621–14645. doi: 10.1109/JIOT.2023.3277466.
[20]	CHENG Nan, LYU Feng, QUAN Wei, et al. Space/aerial-assisted computing offloading for IoT applications: A learning-based approach[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(5): 1117–1129. doi: 10.1109/JSAC.2019.2906789.
[21]	ZHANG Zhenjiang, ZHANG Wenyu, and TSENG F H. Satellite mobile edge computing: Improving QoS of high-speed satellite-terrestrial networks using edge computing techniques[J]. IEEE Network, 2019, 33(1): 70–76. doi: 10.1109/MNET.2018.1800172.
[22]	QIU Chao, YAO Haipeng, YU R F, et al. Deep Q-learning aided networking, caching, and computing resources allocation in software-defined satellite-terrestrial networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(6): 5871–5883. doi: 10.1109/TVT.2019.2907682.
[23]	XIE Renchao, TANG Qinqin, WANG Qiuning, et al. Satellite-terrestrial integrated edge computing networks: Architecture, challenges, and open issues[J]. IEEE Network, 2020, 34(3): 224–231. doi: 10.1109/MNET.011.1900369.
[24]	ZHANG Yalin, GAO Xiaozheng, YUAN Hang, et al. Joint UAV trajectory and power allocation with hybrid FSO/RF for secure space-air-ground communications[J]. IEEE Internet of Things Journal, 2024, 11(19): 31407–31421. doi: 10.1109/JIOT.2024.3419264.
[25]	ZHAI Daosen, ZHANG Ruonan, DU Jianbo, et al. Simultaneous wireless information and power transfer at 5G new frequencies: Channel measurement and network design[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(1): 171–186. doi: 10.1109/JSAC.2018.2872366.
[26]	SONG Yanjie, OU Junwei, WU Jian, et al. A cluster-based genetic optimization method for satellite range scheduling system[J]. Swarm and Evolutionary Computation, 2023, 79: 101316. doi: 10.1016/j.swevo.2023.101316.